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ΒΆModel Training by For Loop:
.csv dosyalarΔ±nΔ±n isimleri aΕaΔΔ±da olduΔu gibi dΓΌzenlenmeli.ΒΆ
final_scout_dummy_EmreDA8127
ΓΔrenci isimleri : ['TugceDA8122', 'TugceDA8122', 'AsliDA8115', 'AysegulDA8116', 'DamlaDA8120', 'EmreDA8119', 'EmreDA8127', 'EsraDA8133', 'GyulferaDA8131', 'HasanDA8121', 'NurdanDA8123', 'SerahsiDA8135', 'SezerDA8134']
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< TugceDA8122's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | power | gears | age | make_model_encoded | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | seller_Private_seller | emission_class_Other | previous_owner_Second_Hand | entertainment_media_count_Upgrated | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 120200.00000 | 75.00000 | 6.00000 | 6.00000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 1 | 46990 | 18995.00000 | 225.00000 | 7.00000 | 2.00000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 16800 | 197000.00000 | 100.00000 | 7.00000 | 7.00000 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 3 | 4690 | 165000.00000 | 90.00000 | 6.00000 | 17.00000 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 |
| 4 | 22550 | 83339.00000 | 90.00000 | 7.00000 | 4.00000 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21602 entries, 0 to 21601 Data columns (total 36 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 21602 non-null int64 1 mileage 21602 non-null float64 2 power 21602 non-null float64 3 gears 21602 non-null float64 4 age 21602 non-null float64 5 make_model_encoded 21602 non-null int64 6 make_Fiat 21602 non-null int64 7 make_Ford 21602 non-null int64 8 make_Hyundai 21602 non-null int64 9 make_Mercedes_Benz 21602 non-null int64 10 make_Nissan 21602 non-null int64 11 make_Opel 21602 non-null int64 12 make_Peugeot 21602 non-null int64 13 make_Renault 21602 non-null int64 14 make_Seat 21602 non-null int64 15 make_Skoda 21602 non-null int64 16 make_Toyota 21602 non-null int64 17 make_Volvo 21602 non-null int64 18 body_type_Convertible 21602 non-null int64 19 body_type_Coupe 21602 non-null int64 20 body_type_Off-Road/Pick-up 21602 non-null int64 21 body_type_Sedan 21602 non-null int64 22 body_type_Station_wagon 21602 non-null int64 23 gearbox_Manual 21602 non-null int64 24 fuel_type_Diesel 21602 non-null int64 25 fuel_type_Electric 21602 non-null int64 26 fuel_type_LPG/CNG 21602 non-null int64 27 fuel_type_Other 21602 non-null int64 28 seller_Private_seller 21602 non-null int64 29 emission_class_Other 21602 non-null int64 30 previous_owner_Second_Hand 21602 non-null int64 31 entertainment_media_count_Upgrated 21602 non-null int64 32 engine_size_cat_High 21602 non-null int64 33 engine_size_cat_Low 21602 non-null int64 34 engine_size_cat_Medium 21602 non-null int64 35 comfort_convenience_cat_standard 21602 non-null int64 dtypes: float64(4), int64(32) memory usage: 5.9 MB
None
(21602, 36)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.85508 0.85632 mae 3452.91793 3399.24570 mse 23715664.27028 23115516.77038 rmse 4869.87313 4807.85990 HuberRegression Model Metrics : train_set test_set R2 0.84861 0.84937 mae 3357.39012 3311.95373 mse 24774956.35866 24233279.80672 rmse 4977.44476 4922.73093 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.85508 0.85632 mae 3452.91793 3399.24570 mse 23715664.27028 23115516.77038 rmse 4869.87313 4807.85990 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99984 0.87690 mae 15.27296 2900.66893 mse 26949.37939 19805002.80999 rmse 164.16266 4450.28121 Random Forest Regressor Model Metrics : train_set test_set R2 0.92849 0.91197 mae 2313.65316 2530.44463 mse 11703165.80525 14162022.21872 rmse 3420.98901 3763.24623 Ada Boost Regressor Model Metrics : train_set test_set R2 0.63879 0.63449 mae 6775.20915 6736.17485 mse 59112743.04705 58804097.64032 rmse 7688.48119 7668.38299 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.91456 0.91171 mae 2563.09153 2598.49437 mse 13981555.99678 14203716.89208 rmse 3739.19189 3768.78188 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97416 0.94129 mae 1507.28322 2067.75065 mse 4229389.76601 9445054.93553 rmse 2056.54802 3073.28081 [LightGBM] [Info] Total Bins 761 [LightGBM] [Info] Number of data points in the train set: 15121, number of used features: 35 [LightGBM] [Info] Start training from score 20967.628001 Light GBM Regressor Model Metrics : train_set test_set R2 0.94907 0.93457 mae 2010.01456 2187.71608 mse 8335045.15868 10525974.38380 rmse 2887.04783 3244.37581 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.85505 0.85621 mae 3453.86343 3400.94291 mse 23721368.33803 23133037.92725 rmse 4870.45874 4809.68169 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96224 0.94585 mae 1772.59354 2017.19474 mse 6178933.23096 8712530.83483 rmse 2485.74601 2951.69965 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6775.20915 | 3453.86343 | 1772.59354 | 15.27296 | 2563.09153 | 3357.39012 | 2010.01456 | 3452.91793 | 2313.65316 | 3452.91793 | 1507.28322 |
| MSE Score | 59112743.04705 | 23721368.33803 | 6178933.23096 | 26949.37939 | 13981555.99678 | 24774956.35866 | 8335045.15868 | 23715664.27028 | 11703165.80525 | 23715664.27028 | 4229389.76601 |
| R2 Score | 0.63449 | 0.85621 | 0.94585 | 0.87690 | 0.91171 | 0.84937 | 0.93457 | 0.85632 | 0.91197 | 0.85632 | 0.94129 |
| RMSE Score | 7668.38299 | 4809.68169 | 2951.69965 | 4450.28121 | 3768.78188 | 4922.73093 | 3244.37581 | 4807.85990 | 3763.24623 | 4807.85990 | 3073.28081 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< TugceDA8122's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | doors | mileage | seats | power | gears | co_emissions | age | fuel_consumption_comb | make_model_encoded | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | location_BE | location_BG | location_DE | location_ES | location_FR | location_IT | location_LU | location_NL | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | type_Employee's_car | type_Pre-registered | type_Used | warranty_Yes | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | paint_non-metallic | seller_Private_seller | drivetrain_Front | drivetrain_Rear | full_service_history_Yes | non_smoker_vehicle_Yes | emission_class_Euro_2 | emission_class_Euro_3 | emission_class_Euro_4 | emission_class_Euro_5 | emission_class_Euro_6 | upholstery_Leather | upholstery_Other | previous_owner_Second_Hand | entertainment_media_count_Upgrated | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | safety_security_category_Middle | safety_security_category_Premium | extras_category_standard | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 5.00000 | 120200.00000 | 5.00000 | 75.00000 | 6.00000 | 101.00000 | 6.00000 | 5.40000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| 1 | 46990 | 5.00000 | 18995.00000 | 5.00000 | 225.00000 | 7.00000 | 170.00000 | 2.00000 | 7.30000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| 2 | 16800 | 5.00000 | 197000.00000 | 5.00000 | 100.00000 | 7.00000 | 82.00000 | 7.00000 | 4.90000 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| 3 | 4690 | 3.00000 | 165000.00000 | 4.00000 | 90.00000 | 6.00000 | 196.00000 | 17.00000 | 8.20000 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| 4 | 22550 | 5.00000 | 83339.00000 | 5.00000 | 90.00000 | 7.00000 | 94.00000 | 4.00000 | 6.30000 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 20801 entries, 0 to 20800 Data columns (total 66 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 20801 non-null int64 1 doors 20801 non-null float64 2 mileage 20801 non-null float64 3 seats 20801 non-null float64 4 power 20801 non-null float64 5 gears 20801 non-null float64 6 co_emissions 20801 non-null float64 7 age 20801 non-null float64 8 fuel_consumption_comb 20801 non-null float64 9 make_model_encoded 20801 non-null int64 10 make_Fiat 20801 non-null int64 11 make_Ford 20801 non-null int64 12 make_Hyundai 20801 non-null int64 13 make_Mercedes_Benz 20801 non-null int64 14 make_Nissan 20801 non-null int64 15 make_Opel 20801 non-null int64 16 make_Peugeot 20801 non-null int64 17 make_Renault 20801 non-null int64 18 make_Seat 20801 non-null int64 19 make_Skoda 20801 non-null int64 20 make_Toyota 20801 non-null int64 21 make_Volvo 20801 non-null int64 22 location_BE 20801 non-null int64 23 location_BG 20801 non-null int64 24 location_DE 20801 non-null int64 25 location_ES 20801 non-null int64 26 location_FR 20801 non-null int64 27 location_IT 20801 non-null int64 28 location_LU 20801 non-null int64 29 location_NL 20801 non-null int64 30 body_type_Convertible 20801 non-null int64 31 body_type_Coupe 20801 non-null int64 32 body_type_Off-Road/Pick-up 20801 non-null int64 33 body_type_Sedan 20801 non-null int64 34 body_type_Station_wagon 20801 non-null int64 35 type_Employee's_car 20801 non-null int64 36 type_Pre-registered 20801 non-null int64 37 type_Used 20801 non-null int64 38 warranty_Yes 20801 non-null int64 39 gearbox_Manual 20801 non-null int64 40 fuel_type_Diesel 20801 non-null int64 41 fuel_type_Electric 20801 non-null int64 42 fuel_type_LPG/CNG 20801 non-null int64 43 fuel_type_Other 20801 non-null int64 44 paint_non-metallic 20801 non-null int64 45 seller_Private_seller 20801 non-null int64 46 drivetrain_Front 20801 non-null int64 47 drivetrain_Rear 20801 non-null int64 48 full_service_history_Yes 20801 non-null int64 49 non_smoker_vehicle_Yes 20801 non-null int64 50 emission_class_Euro_2 20801 non-null int64 51 emission_class_Euro_3 20801 non-null int64 52 emission_class_Euro_4 20801 non-null int64 53 emission_class_Euro_5 20801 non-null int64 54 emission_class_Euro_6 20801 non-null int64 55 upholstery_Leather 20801 non-null int64 56 upholstery_Other 20801 non-null int64 57 previous_owner_Second_Hand 20801 non-null int64 58 entertainment_media_count_Upgrated 20801 non-null int64 59 engine_size_cat_High 20801 non-null int64 60 engine_size_cat_Low 20801 non-null int64 61 engine_size_cat_Medium 20801 non-null int64 62 safety_security_category_Middle 20801 non-null int64 63 safety_security_category_Premium 20801 non-null int64 64 extras_category_standard 20801 non-null int64 65 comfort_convenience_cat_standard 20801 non-null int64 dtypes: float64(8), int64(58) memory usage: 10.5 MB
None
(20801, 66)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.87753 0.87220 mae 3082.35417 3129.28875 mse 18807814.86400 19668927.49323 rmse 4336.79777 4434.96646 HuberRegression Model Metrics : train_set test_set R2 0.87171 0.86683 mae 3022.17174 3054.74900 mse 19700285.36178 20495232.13903 rmse 4438.50035 4527.16602 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.87753 0.87220 mae 3082.35417 3129.28875 mse 18807814.86400 19668927.49323 rmse 4336.79777 4434.96646 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99992 0.86982 mae 7.58624 2869.40210 mse 12890.77297 20035011.54336 rmse 113.53754 4476.04865 Random Forest Regressor Model Metrics : train_set test_set R2 0.93502 0.90871 mae 2175.49417 2508.59654 mse 9978510.73750 14049578.99558 rmse 3158.87808 3748.27680 Ada Boost Regressor Model Metrics : train_set test_set R2 0.72520 0.71634 mae 5626.33088 5699.63110 mse 42199273.43863 43656778.34954 rmse 6496.09678 6607.32763 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.92435 0.91768 mae 2381.63989 2440.16997 mse 11617920.78296 12669818.91848 rmse 3408.50712 3559.46891 XG Boosting Regressor Model Metrics : train_set test_set R2 0.98019 0.94349 mae 1288.70307 1957.09445 mse 3042386.04796 8697665.80240 rmse 1744.24369 2949.18053 [LightGBM] [Info] Total Bins 1058 [LightGBM] [Info] Number of data points in the train set: 14560, number of used features: 61 [LightGBM] [Info] Start training from score 20553.206868 Light GBM Regressor Model Metrics : train_set test_set R2 0.95952 0.94123 mae 1765.94161 2034.62048 mse 6216262.28004 9045618.50175 rmse 2493.24333 3007.59347 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.87741 0.87203 mae 3085.13484 3130.23658 mse 18825092.92124 19695935.93407 rmse 4338.78934 4438.01036 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.97186 0.95003 mae 1505.74803 1845.39427 mse 4320956.56068 7691212.31580 rmse 2078.69107 2773.30350 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5626.33088 | 3085.13484 | 1505.74803 | 7.58624 | 2381.63989 | 3022.17174 | 1765.94161 | 3082.35417 | 2175.49417 | 3082.35417 | 1288.70307 |
| MSE Score | 42199273.43863 | 18825092.92124 | 4320956.56068 | 12890.77297 | 11617920.78296 | 19700285.36178 | 6216262.28004 | 18807814.86400 | 9978510.73750 | 18807814.86400 | 3042386.04796 |
| R2 Score | 0.71634 | 0.87203 | 0.95003 | 0.86982 | 0.91768 | 0.86683 | 0.94123 | 0.87220 | 0.90871 | 0.87220 | 0.94349 |
| RMSE Score | 6607.32763 | 4438.01036 | 2773.30350 | 4476.04865 | 3559.46891 | 4527.16602 | 3007.59347 | 4434.96646 | 3748.27680 | 4434.96646 | 2949.18053 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< AsliDA8115's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | power_kW | fuel_consumption_comb | age | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 96 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 110 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 110 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 power_kW 24529 non-null int64 7 fuel_consumption_comb 24529 non-null int64 8 age 24529 non-null int64 9 make_model_factorized 24529 non-null int64 10 body_type__Convertible 24529 non-null int64 11 body_type__Coupe 24529 non-null int64 12 body_type__Off-Road/Pick-up 24529 non-null int64 13 body_type__Sedan 24529 non-null int64 14 body_type__Station_wagon 24529 non-null int64 15 warranty_Yes 24529 non-null int64 16 gearbox_Manual 24529 non-null int64 17 gearbox_Semi-automatic 24529 non-null int64 18 fuel_type_Diesel 24529 non-null int64 19 fuel_type_Electric 24529 non-null int64 20 fuel_type_LPG 24529 non-null int64 21 colour_Black 24529 non-null int64 22 colour_Blue 24529 non-null int64 23 colour_Bronze 24529 non-null int64 24 colour_Brown 24529 non-null int64 25 colour_Gold 24529 non-null int64 26 colour_Green 24529 non-null int64 27 colour_Grey 24529 non-null int64 28 colour_Orange 24529 non-null int64 29 colour_Red 24529 non-null int64 30 colour_Silver 24529 non-null int64 31 colour_Violet 24529 non-null int64 32 colour_White 24529 non-null int64 33 colour_Yellow 24529 non-null int64 34 drivetrain_Front 24529 non-null int64 35 drivetrain_Rear 24529 non-null int64 36 non_smoker_Yes 24529 non-null int64 37 emission_sticker_No_sticker 24529 non-null int64 38 emission_sticker_Red 24529 non-null int64 39 emission_sticker_Yellow 24529 non-null int64 40 upholstery_Full_leather 24529 non-null int64 41 upholstery_Other 24529 non-null int64 42 upholstery_Part_leather 24529 non-null int64 43 upholstery_Velour 24529 non-null int64 44 upholstery_alcantara 24529 non-null int64 45 safety_security_package_Basic 24529 non-null int64 46 safety_security_package_Enhanced 24529 non-null int64 47 comfort_convenience_package_Basic 24529 non-null int64 48 comfort_convenience_package_Enhanced 24529 non-null int64 49 ent_media_package_Basic 24529 non-null int64 50 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(51) memory usage: 9.5 MB
None
(24529, 51)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 HuberRegression Model Metrics : train_set test_set R2 0.74842 0.75294 mae 4644.22247 4622.21707 mse 56492043.07455 55071902.37974 rmse 7516.11888 7421.04456 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.84257 mae 1.14543 3307.79947 mse 1328.78718 35092847.19058 rmse 36.45253 5923.92161 Random Forest Regressor Model Metrics : train_set test_set R2 0.92145 0.88511 mae 2565.42971 2993.96732 mse 17639509.41618 25610097.71287 rmse 4199.94160 5060.64203 Ada Boost Regressor Model Metrics : train_set test_set R2 0.55878 0.53899 mae 8411.11711 8462.59512 mse 99077157.86718 102761542.31176 rmse 9953.75094 10137.13679 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.89748 0.87252 mae 3061.44094 3244.39116 mse 23020963.52384 28416434.69317 rmse 4798.01662 5330.70677 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97580 0.91265 mae 1657.36035 2539.00851 mse 5433075.53408 19469922.88249 rmse 2330.89587 4412.47356 [LightGBM] [Info] Total Bins 1322 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.94720 0.90983 mae 2283.47161 2674.31067 mse 11855808.80354 20100010.33039 rmse 3443.22651 4483.30351 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.76655 0.76659 mae 4839.14450 4850.98532 mse 52421491.42523 52028553.03376 rmse 7240.26874 7213.08208 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96380 0.92139 mae 1976.12172 2477.60347 mse 8127895.32365 17521714.02079 rmse 2850.94639 4185.89465 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8411.11711 | 4839.14450 | 1976.12172 | 1.14543 | 3061.44094 | 4644.22247 | 2283.47161 | 4841.22970 | 2565.42971 | 4841.22970 | 1657.36035 |
| MSE Score | 99077157.86718 | 52421491.42523 | 8127895.32365 | 1328.78718 | 23020963.52384 | 56492043.07455 | 11855808.80354 | 52412344.82297 | 17639509.41618 | 52412344.82297 | 5433075.53408 |
| R2 Score | 0.53899 | 0.76659 | 0.92139 | 0.84257 | 0.87252 | 0.75294 | 0.90983 | 0.76651 | 0.88511 | 0.76651 | 0.91265 |
| RMSE Score | 10137.13679 | 7213.08208 | 4185.89465 | 5923.92161 | 5330.70677 | 7421.04456 | 4483.30351 | 7214.29080 | 5060.64203 | 7214.29080 | 4412.47356 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< AysegulDA8116's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> !!!!!!!!AysegulDA8116's DataFrame has non-numeric value(s) !!!!!!!!!! <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< DamlaDA8120's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | cylinders | power_kW | fuel_consumption_comb | age | z_score_gears | price_zscore | mileage_zscore | cylinders_zscore | z_score | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | location_country_Belgium | location_country_Bulgaria | location_country_Denmark | location_country_Estonia | location_country_France | location_country_Germany | location_country_Italy | location_country_Luxembourg | location_country_Netherlands | location_country_Spain | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 4 | 85 | 4 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 4 | 85 | 4 | 0 | 0 | 0 | -1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 4 | 96 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 4 | 110 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 4 | 110 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 67 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 cylinders 24529 non-null int64 7 power_kW 24529 non-null int64 8 fuel_consumption_comb 24529 non-null int64 9 age 24529 non-null int64 10 z_score_gears 24529 non-null int64 11 price_zscore 24529 non-null int64 12 mileage_zscore 24529 non-null int64 13 cylinders_zscore 24529 non-null int64 14 z_score 24529 non-null int64 15 make_model_factorized 24529 non-null int64 16 body_type__Convertible 24529 non-null int64 17 body_type__Coupe 24529 non-null int64 18 body_type__Off-Road/Pick-up 24529 non-null int64 19 body_type__Sedan 24529 non-null int64 20 body_type__Station_wagon 24529 non-null int64 21 warranty_Yes 24529 non-null int64 22 gearbox_Manual 24529 non-null int64 23 gearbox_Semi-automatic 24529 non-null int64 24 fuel_type_Diesel 24529 non-null int64 25 fuel_type_Electric 24529 non-null int64 26 fuel_type_LPG 24529 non-null int64 27 colour_Black 24529 non-null int64 28 colour_Blue 24529 non-null int64 29 colour_Bronze 24529 non-null int64 30 colour_Brown 24529 non-null int64 31 colour_Gold 24529 non-null int64 32 colour_Green 24529 non-null int64 33 colour_Grey 24529 non-null int64 34 colour_Orange 24529 non-null int64 35 colour_Red 24529 non-null int64 36 colour_Silver 24529 non-null int64 37 colour_Violet 24529 non-null int64 38 colour_White 24529 non-null int64 39 colour_Yellow 24529 non-null int64 40 drivetrain_Front 24529 non-null int64 41 drivetrain_Rear 24529 non-null int64 42 non_smoker_Yes 24529 non-null int64 43 emission_sticker_No_sticker 24529 non-null int64 44 emission_sticker_Red 24529 non-null int64 45 emission_sticker_Yellow 24529 non-null int64 46 upholstery_Full_leather 24529 non-null int64 47 upholstery_Other 24529 non-null int64 48 upholstery_Part_leather 24529 non-null int64 49 upholstery_Velour 24529 non-null int64 50 upholstery_alcantara 24529 non-null int64 51 location_country_Belgium 24529 non-null int64 52 location_country_Bulgaria 24529 non-null int64 53 location_country_Denmark 24529 non-null int64 54 location_country_Estonia 24529 non-null int64 55 location_country_France 24529 non-null int64 56 location_country_Germany 24529 non-null int64 57 location_country_Italy 24529 non-null int64 58 location_country_Luxembourg 24529 non-null int64 59 location_country_Netherlands 24529 non-null int64 60 location_country_Spain 24529 non-null int64 61 safety_security_package_Basic 24529 non-null int64 62 safety_security_package_Enhanced 24529 non-null int64 63 comfort_convenience_package_Basic 24529 non-null int64 64 comfort_convenience_package_Enhanced 24529 non-null int64 65 ent_media_package_Basic 24529 non-null int64 66 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(67) memory usage: 12.5 MB
None
(24529, 67)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.87819 0.87777 mae 3867.34385 3860.60787 mse 27352551.31604 27246445.88006 rmse 5229.96666 5219.81282 HuberRegression Model Metrics : train_set test_set R2 0.87139 0.87473 mae 3779.31754 3728.59401 mse 28880527.45064 27923686.25613 rmse 5374.06061 5284.28673 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.87819 0.87777 mae 3867.34385 3860.60787 mse 27352551.31604 27246445.88006 rmse 5229.96666 5219.81282 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.90445 mae 1.14543 2899.39829 mse 1328.78718 21298712.39190 rmse 36.45253 4615.05280 Random Forest Regressor Model Metrics : train_set test_set R2 0.95232 0.93459 mae 2293.86789 2629.52368 mse 10707556.83618 14579689.96813 rmse 3272.24034 3818.33602 Ada Boost Regressor Model Metrics : train_set test_set R2 0.82975 0.82493 mae 5180.06950 5192.50295 mse 38230725.55051 39023099.56670 rmse 6183.10000 6246.84717 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.93837 0.93013 mae 2639.61364 2762.50707 mse 13838441.07150 15573666.65758 rmse 3720.00552 3946.34852 XG Boosting Regressor Model Metrics : train_set test_set R2 0.98262 0.95179 mae 1445.73207 2138.41078 mse 3903532.46051 10746033.93823 rmse 1975.73593 3278.11439 [LightGBM] [Info] Total Bins 1372 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 63 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.96448 0.94833 mae 2005.60132 2291.96333 mse 7976521.64817 11516896.84416 rmse 2824.27365 3393.65538 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.87812 0.87782 mae 3868.10088 3859.50166 mse 27367611.17300 27234698.09598 rmse 5231.40623 5218.68739 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.97412 0.95265 mae 1743.70527 2150.43883 mse 5812190.67280 10554721.84414 rmse 2410.84854 3248.80314 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5180.06950 | 3868.10088 | 1743.70527 | 1.14543 | 2639.61364 | 3779.31754 | 2005.60132 | 3867.34385 | 2293.86789 | 3867.34385 | 1445.73207 |
| MSE Score | 38230725.55051 | 27367611.17300 | 5812190.67280 | 1328.78718 | 13838441.07150 | 28880527.45064 | 7976521.64817 | 27352551.31604 | 10707556.83618 | 27352551.31604 | 3903532.46051 |
| R2 Score | 0.82493 | 0.87782 | 0.95265 | 0.90445 | 0.93013 | 0.87473 | 0.94833 | 0.87777 | 0.93459 | 0.87777 | 0.95179 |
| RMSE Score | 6246.84717 | 5218.68739 | 3248.80314 | 4615.05280 | 3946.34852 | 5284.28673 | 3393.65538 | 5219.81282 | 3818.33602 | 5219.81282 | 3278.11439 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EmreDA8119's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | doors | mileage | power | gears | age | make_model_encoded | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | location_BE | location_BG | location_DE | location_ES | location_FR | location_IT | location_LU | location_NL | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | type_Employee's_car | type_Pre-registered | type_Used | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | seller_Private_seller | emission_class_Other | entertainment_media_count_Upgrated | engine_size_cat_Extreme | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 5.00000 | 120200.00000 | 75.00000 | 6.00000 | 6.00000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 1 | 46990 | 5.00000 | 18995.00000 | 225.00000 | 7.00000 | 2.00000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 16800 | 5.00000 | 197000.00000 | 100.00000 | 7.00000 | 7.00000 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 3 | 4690 | 3.00000 | 165000.00000 | 90.00000 | 6.00000 | 17.00000 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 |
| 4 | 22550 | 5.00000 | 83339.00000 | 90.00000 | 7.00000 | 4.00000 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21617 entries, 0 to 21616 Data columns (total 48 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 21617 non-null int64 1 doors 21617 non-null float64 2 mileage 21617 non-null float64 3 power 21617 non-null float64 4 gears 21617 non-null float64 5 age 21617 non-null float64 6 make_model_encoded 21617 non-null int64 7 make_Fiat 21617 non-null int64 8 make_Ford 21617 non-null int64 9 make_Hyundai 21617 non-null int64 10 make_Mercedes_Benz 21617 non-null int64 11 make_Nissan 21617 non-null int64 12 make_Opel 21617 non-null int64 13 make_Peugeot 21617 non-null int64 14 make_Renault 21617 non-null int64 15 make_Seat 21617 non-null int64 16 make_Skoda 21617 non-null int64 17 make_Toyota 21617 non-null int64 18 make_Volvo 21617 non-null int64 19 location_BE 21617 non-null int64 20 location_BG 21617 non-null int64 21 location_DE 21617 non-null int64 22 location_ES 21617 non-null int64 23 location_FR 21617 non-null int64 24 location_IT 21617 non-null int64 25 location_LU 21617 non-null int64 26 location_NL 21617 non-null int64 27 body_type_Convertible 21617 non-null int64 28 body_type_Coupe 21617 non-null int64 29 body_type_Off-Road/Pick-up 21617 non-null int64 30 body_type_Sedan 21617 non-null int64 31 body_type_Station_wagon 21617 non-null int64 32 type_Employee's_car 21617 non-null int64 33 type_Pre-registered 21617 non-null int64 34 type_Used 21617 non-null int64 35 gearbox_Manual 21617 non-null int64 36 fuel_type_Diesel 21617 non-null int64 37 fuel_type_Electric 21617 non-null int64 38 fuel_type_LPG/CNG 21617 non-null int64 39 fuel_type_Other 21617 non-null int64 40 seller_Private_seller 21617 non-null int64 41 emission_class_Other 21617 non-null int64 42 entertainment_media_count_Upgrated 21617 non-null int64 43 engine_size_cat_Extreme 21617 non-null int64 44 engine_size_cat_High 21617 non-null int64 45 engine_size_cat_Low 21617 non-null int64 46 engine_size_cat_Medium 21617 non-null int64 47 comfort_convenience_cat_standard 21617 non-null int64 dtypes: float64(5), int64(43) memory usage: 7.9 MB
None
(21617, 48)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.86662 0.85833 mae 3312.89057 3356.44588 mse 21803984.47824 22817350.10638 rmse 4669.47368 4776.75100 HuberRegression Model Metrics : train_set test_set R2 0.86038 0.85145 mae 3228.75006 3278.13085 mse 22824013.23774 23924189.45598 rmse 4777.44840 4891.23598 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.86662 0.85833 mae 3312.89057 3356.44588 mse 21803984.47824 22817350.10638 rmse 4669.47368 4776.75100 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99987 0.87783 mae 14.02929 2863.65947 mse 21808.54950 19676078.20979 rmse 147.67718 4435.77256 Random Forest Regressor Model Metrics : train_set test_set R2 0.93119 0.90364 mae 2287.87427 2606.13144 mse 11248370.79919 15518489.12648 rmse 3353.85909 3939.35136 Ada Boost Regressor Model Metrics : train_set test_set R2 0.70466 0.69138 mae 6045.09850 6116.04050 mse 48280654.51951 49705215.07497 rmse 6948.42820 7050.19256 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.91865 0.90485 mae 2520.98206 2638.05833 mse 13299033.38154 15324858.41518 rmse 3646.78398 3914.69774 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97655 0.94007 mae 1431.37568 2066.23737 mse 3833295.54169 9651699.26309 rmse 1957.88037 3106.71841 [LightGBM] [Info] Total Bins 783 [LightGBM] [Info] Number of data points in the train set: 15131, number of used features: 46 [LightGBM] [Info] Start training from score 20942.666975 Light GBM Regressor Model Metrics : train_set test_set R2 0.95392 0.93279 mae 1921.63105 2177.07698 mse 7532223.41276 10824218.12242 rmse 2744.48965 3290.01795 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.86655 0.85820 mae 3313.15201 3357.19933 mse 21816008.50423 22837886.29711 rmse 4670.76102 4778.90011 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96686 0.94495 mae 1674.97405 1991.42915 mse 5418034.60978 8866152.98383 rmse 2327.66720 2977.60860 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6045.09850 | 3313.15201 | 1674.97405 | 14.02929 | 2520.98206 | 3228.75006 | 1921.63105 | 3312.89057 | 2287.87427 | 3312.89057 | 1431.37568 |
| MSE Score | 48280654.51951 | 21816008.50423 | 5418034.60978 | 21808.54950 | 13299033.38154 | 22824013.23774 | 7532223.41276 | 21803984.47824 | 11248370.79919 | 21803984.47824 | 3833295.54169 |
| R2 Score | 0.69138 | 0.85820 | 0.94495 | 0.87783 | 0.90485 | 0.85145 | 0.93279 | 0.85833 | 0.90364 | 0.85833 | 0.94007 |
| RMSE Score | 7050.19256 | 4778.90011 | 2977.60860 | 4435.77256 | 3914.69774 | 4891.23598 | 3290.01795 | 4776.75100 | 3939.35136 | 4776.75100 | 3106.71841 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EmreDA8127's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | power_kW | fuel_consumption_comb | age | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 96 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 110 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 110 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 power_kW 24529 non-null int64 7 fuel_consumption_comb 24529 non-null int64 8 age 24529 non-null int64 9 make_model_factorized 24529 non-null int64 10 body_type__Convertible 24529 non-null int64 11 body_type__Coupe 24529 non-null int64 12 body_type__Off-Road/Pick-up 24529 non-null int64 13 body_type__Sedan 24529 non-null int64 14 body_type__Station_wagon 24529 non-null int64 15 warranty_Yes 24529 non-null int64 16 gearbox_Manual 24529 non-null int64 17 gearbox_Semi-automatic 24529 non-null int64 18 fuel_type_Diesel 24529 non-null int64 19 fuel_type_Electric 24529 non-null int64 20 fuel_type_LPG 24529 non-null int64 21 colour_Black 24529 non-null int64 22 colour_Blue 24529 non-null int64 23 colour_Bronze 24529 non-null int64 24 colour_Brown 24529 non-null int64 25 colour_Gold 24529 non-null int64 26 colour_Green 24529 non-null int64 27 colour_Grey 24529 non-null int64 28 colour_Orange 24529 non-null int64 29 colour_Red 24529 non-null int64 30 colour_Silver 24529 non-null int64 31 colour_Violet 24529 non-null int64 32 colour_White 24529 non-null int64 33 colour_Yellow 24529 non-null int64 34 drivetrain_Front 24529 non-null int64 35 drivetrain_Rear 24529 non-null int64 36 non_smoker_Yes 24529 non-null int64 37 emission_sticker_No_sticker 24529 non-null int64 38 emission_sticker_Red 24529 non-null int64 39 emission_sticker_Yellow 24529 non-null int64 40 upholstery_Full_leather 24529 non-null int64 41 upholstery_Other 24529 non-null int64 42 upholstery_Part_leather 24529 non-null int64 43 upholstery_Velour 24529 non-null int64 44 upholstery_alcantara 24529 non-null int64 45 safety_security_package_Basic 24529 non-null int64 46 safety_security_package_Enhanced 24529 non-null int64 47 comfort_convenience_package_Basic 24529 non-null int64 48 comfort_convenience_package_Enhanced 24529 non-null int64 49 ent_media_package_Basic 24529 non-null int64 50 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(51) memory usage: 9.5 MB
None
(24529, 51)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 HuberRegression Model Metrics : train_set test_set R2 0.74842 0.75294 mae 4644.22247 4622.21707 mse 56492043.07455 55071902.37974 rmse 7516.11888 7421.04456 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.84257 mae 1.14543 3307.79947 mse 1328.78718 35092847.19058 rmse 36.45253 5923.92161 Random Forest Regressor Model Metrics : train_set test_set R2 0.92145 0.88511 mae 2565.42971 2993.96732 mse 17639509.41618 25610097.71287 rmse 4199.94160 5060.64203 Ada Boost Regressor Model Metrics : train_set test_set R2 0.55878 0.53899 mae 8411.11711 8462.59512 mse 99077157.86718 102761542.31176 rmse 9953.75094 10137.13679 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.89748 0.87252 mae 3061.44094 3244.39116 mse 23020963.52384 28416434.69317 rmse 4798.01662 5330.70677 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97580 0.91265 mae 1657.36035 2539.00851 mse 5433075.53408 19469922.88249 rmse 2330.89587 4412.47356 [LightGBM] [Info] Total Bins 1322 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.94720 0.90983 mae 2283.47161 2674.31067 mse 11855808.80354 20100010.33039 rmse 3443.22651 4483.30351 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.76655 0.76659 mae 4839.14450 4850.98532 mse 52421491.42523 52028553.03376 rmse 7240.26874 7213.08208 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96380 0.92139 mae 1976.12172 2477.60347 mse 8127895.32365 17521714.02079 rmse 2850.94639 4185.89465 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8411.11711 | 4839.14450 | 1976.12172 | 1.14543 | 3061.44094 | 4644.22247 | 2283.47161 | 4841.22970 | 2565.42971 | 4841.22970 | 1657.36035 |
| MSE Score | 99077157.86718 | 52421491.42523 | 8127895.32365 | 1328.78718 | 23020963.52384 | 56492043.07455 | 11855808.80354 | 52412344.82297 | 17639509.41618 | 52412344.82297 | 5433075.53408 |
| R2 Score | 0.53899 | 0.76659 | 0.92139 | 0.84257 | 0.87252 | 0.75294 | 0.90983 | 0.76651 | 0.88511 | 0.76651 | 0.91265 |
| RMSE Score | 10137.13679 | 7213.08208 | 4185.89465 | 5923.92161 | 5330.70677 | 7421.04456 | 4483.30351 | 7214.29080 | 5060.64203 | 7214.29080 | 4412.47356 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EsraDA8133's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | power | gears | age | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | seller_Private_seller | full_service_history_Yes | non_smoker_vehicle_Yes | emission_class_Other | upholstery_Leather | upholstery_Other | previous_owner_Second_Hand | entertainment_media_count_Upgrated | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 120200.00000 | 75.00000 | 6.00000 | 6.00000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 1 | 46990 | 18995.00000 | 225.00000 | 7.00000 | 2.00000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 16800 | 197000.00000 | 100.00000 | 7.00000 | 7.00000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 3 | 4690 | 165000.00000 | 90.00000 | 6.00000 | 17.00000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 |
| 4 | 22550 | 83339.00000 | 90.00000 | 7.00000 | 4.00000 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21614 entries, 0 to 21613 Data columns (total 39 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 21614 non-null int64 1 mileage 21614 non-null float64 2 power 21614 non-null float64 3 gears 21614 non-null float64 4 age 21614 non-null float64 5 make_Fiat 21614 non-null int64 6 make_Ford 21614 non-null int64 7 make_Hyundai 21614 non-null int64 8 make_Mercedes_Benz 21614 non-null int64 9 make_Nissan 21614 non-null int64 10 make_Opel 21614 non-null int64 11 make_Peugeot 21614 non-null int64 12 make_Renault 21614 non-null int64 13 make_Seat 21614 non-null int64 14 make_Skoda 21614 non-null int64 15 make_Toyota 21614 non-null int64 16 make_Volvo 21614 non-null int64 17 body_type_Convertible 21614 non-null int64 18 body_type_Coupe 21614 non-null int64 19 body_type_Off-Road/Pick-up 21614 non-null int64 20 body_type_Sedan 21614 non-null int64 21 body_type_Station_wagon 21614 non-null int64 22 gearbox_Manual 21614 non-null int64 23 fuel_type_Diesel 21614 non-null int64 24 fuel_type_Electric 21614 non-null int64 25 fuel_type_LPG/CNG 21614 non-null int64 26 fuel_type_Other 21614 non-null int64 27 seller_Private_seller 21614 non-null int64 28 full_service_history_Yes 21614 non-null int64 29 non_smoker_vehicle_Yes 21614 non-null int64 30 emission_class_Other 21614 non-null int64 31 upholstery_Leather 21614 non-null int64 32 upholstery_Other 21614 non-null int64 33 previous_owner_Second_Hand 21614 non-null int64 34 entertainment_media_count_Upgrated 21614 non-null int64 35 engine_size_cat_High 21614 non-null int64 36 engine_size_cat_Low 21614 non-null int64 37 engine_size_cat_Medium 21614 non-null int64 38 comfort_convenience_cat_standard 21614 non-null int64 dtypes: float64(4), int64(35) memory usage: 6.4 MB
None
(21614, 39)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.85572 0.85189 mae 3439.46521 3450.23142 mse 23602120.65529 23816739.52861 rmse 4858.20138 4880.23970 HuberRegression Model Metrics : train_set test_set R2 0.84930 0.84420 mae 3333.51086 3368.34798 mse 24652134.56788 25053323.43071 rmse 4965.09160 5005.32950 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.85572 0.85189 mae 3439.46521 3450.23142 mse 23602120.65529 23816739.52861 rmse 4858.20138 4880.23970 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99980 0.85108 mae 15.39357 3135.57355 mse 32626.07141 23947007.28512 rmse 180.62688 4893.56795 Random Forest Regressor Model Metrics : train_set test_set R2 0.92782 0.89971 mae 2347.47044 2662.04003 mse 11807038.02992 16127153.55726 rmse 3436.13708 4015.86274 Ada Boost Regressor Model Metrics : train_set test_set R2 0.71887 0.70920 mae 5777.21584 5806.06945 mse 45989889.55000 46761752.32734 rmse 6781.58459 6838.25653 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.91329 0.90278 mae 2595.94631 2666.05295 mse 14184987.69031 15633793.58182 rmse 3766.29628 3953.95923 XG Boosting Regressor Model Metrics : train_set test_set R2 0.96847 0.92490 mae 1632.52562 2287.87443 mse 5157898.81686 12076072.58719 rmse 2271.10079 3475.06440 [LightGBM] [Info] Total Bins 557 [LightGBM] [Info] Number of data points in the train set: 15129, number of used features: 38 [LightGBM] [Info] Start training from score 20941.412387 Light GBM Regressor Model Metrics : train_set test_set R2 0.94528 0.92095 mae 2073.46370 2342.80021 mse 8950888.66248 12711159.15825 rmse 2991.80358 3565.27126 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.85570 0.85179 mae 3438.04101 3450.46944 mse 23604856.51960 23833064.07750 rmse 4858.48294 4881.91193 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.95786 0.92901 mae 1836.46852 2204.36621 mse 6893465.49282 11414997.41655 rmse 2625.54099 3378.60880 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5777.21584 | 3438.04101 | 1836.46852 | 15.39357 | 2595.94631 | 3333.51086 | 2073.46370 | 3439.46521 | 2347.47044 | 3439.46521 | 1632.52562 |
| MSE Score | 45989889.55000 | 23604856.51960 | 6893465.49282 | 32626.07141 | 14184987.69031 | 24652134.56788 | 8950888.66248 | 23602120.65529 | 11807038.02992 | 23602120.65529 | 5157898.81686 |
| R2 Score | 0.70920 | 0.85179 | 0.92901 | 0.85108 | 0.90278 | 0.84420 | 0.92095 | 0.85189 | 0.89971 | 0.85189 | 0.92490 |
| RMSE Score | 6838.25653 | 4881.91193 | 3378.60880 | 4893.56795 | 3953.95923 | 5005.32950 | 3565.27126 | 4880.23970 | 4015.86274 | 4880.23970 | 3475.06440 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< GyulferaDA8131's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | power_kW | fuel_consumption_comb | age | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 96 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 110 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 110 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 power_kW 24529 non-null int64 7 fuel_consumption_comb 24529 non-null int64 8 age 24529 non-null int64 9 make_model_factorized 24529 non-null int64 10 body_type__Convertible 24529 non-null int64 11 body_type__Coupe 24529 non-null int64 12 body_type__Off-Road/Pick-up 24529 non-null int64 13 body_type__Sedan 24529 non-null int64 14 body_type__Station_wagon 24529 non-null int64 15 warranty_Yes 24529 non-null int64 16 gearbox_Manual 24529 non-null int64 17 gearbox_Semi-automatic 24529 non-null int64 18 fuel_type_Diesel 24529 non-null int64 19 fuel_type_Electric 24529 non-null int64 20 fuel_type_LPG 24529 non-null int64 21 colour_Black 24529 non-null int64 22 colour_Blue 24529 non-null int64 23 colour_Bronze 24529 non-null int64 24 colour_Brown 24529 non-null int64 25 colour_Gold 24529 non-null int64 26 colour_Green 24529 non-null int64 27 colour_Grey 24529 non-null int64 28 colour_Orange 24529 non-null int64 29 colour_Red 24529 non-null int64 30 colour_Silver 24529 non-null int64 31 colour_Violet 24529 non-null int64 32 colour_White 24529 non-null int64 33 colour_Yellow 24529 non-null int64 34 drivetrain_Front 24529 non-null int64 35 drivetrain_Rear 24529 non-null int64 36 non_smoker_Yes 24529 non-null int64 37 emission_sticker_No_sticker 24529 non-null int64 38 emission_sticker_Red 24529 non-null int64 39 emission_sticker_Yellow 24529 non-null int64 40 upholstery_Full_leather 24529 non-null int64 41 upholstery_Other 24529 non-null int64 42 upholstery_Part_leather 24529 non-null int64 43 upholstery_Velour 24529 non-null int64 44 upholstery_alcantara 24529 non-null int64 45 safety_security_package_Basic 24529 non-null int64 46 safety_security_package_Enhanced 24529 non-null int64 47 comfort_convenience_package_Basic 24529 non-null int64 48 comfort_convenience_package_Enhanced 24529 non-null int64 49 ent_media_package_Basic 24529 non-null int64 50 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(51) memory usage: 9.5 MB
None
(24529, 51)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 HuberRegression Model Metrics : train_set test_set R2 0.74842 0.75294 mae 4644.22247 4622.21707 mse 56492043.07455 55071902.37974 rmse 7516.11888 7421.04456 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.84257 mae 1.14543 3307.79947 mse 1328.78718 35092847.19058 rmse 36.45253 5923.92161 Random Forest Regressor Model Metrics : train_set test_set R2 0.92145 0.88511 mae 2565.42971 2993.96732 mse 17639509.41618 25610097.71287 rmse 4199.94160 5060.64203 Ada Boost Regressor Model Metrics : train_set test_set R2 0.55878 0.53899 mae 8411.11711 8462.59512 mse 99077157.86718 102761542.31176 rmse 9953.75094 10137.13679 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.89748 0.87252 mae 3061.44094 3244.39116 mse 23020963.52384 28416434.69317 rmse 4798.01662 5330.70677 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97580 0.91265 mae 1657.36035 2539.00851 mse 5433075.53408 19469922.88249 rmse 2330.89587 4412.47356 [LightGBM] [Info] Total Bins 1322 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.94720 0.90983 mae 2283.47161 2674.31067 mse 11855808.80354 20100010.33039 rmse 3443.22651 4483.30351 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.76655 0.76659 mae 4839.14450 4850.98532 mse 52421491.42523 52028553.03376 rmse 7240.26874 7213.08208 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96380 0.92139 mae 1976.12172 2477.60347 mse 8127895.32365 17521714.02079 rmse 2850.94639 4185.89465 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8411.11711 | 4839.14450 | 1976.12172 | 1.14543 | 3061.44094 | 4644.22247 | 2283.47161 | 4841.22970 | 2565.42971 | 4841.22970 | 1657.36035 |
| MSE Score | 99077157.86718 | 52421491.42523 | 8127895.32365 | 1328.78718 | 23020963.52384 | 56492043.07455 | 11855808.80354 | 52412344.82297 | 17639509.41618 | 52412344.82297 | 5433075.53408 |
| R2 Score | 0.53899 | 0.76659 | 0.92139 | 0.84257 | 0.87252 | 0.75294 | 0.90983 | 0.76651 | 0.88511 | 0.76651 | 0.91265 |
| RMSE Score | 10137.13679 | 7213.08208 | 4185.89465 | 5923.92161 | 5330.70677 | 7421.04456 | 4483.30351 | 7214.29080 | 5060.64203 | 7214.29080 | 4412.47356 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HasanDA8121's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | power_kW | fuel_consumption_comb | age | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 96 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 110 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 110 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 power_kW 24529 non-null int64 7 fuel_consumption_comb 24529 non-null int64 8 age 24529 non-null int64 9 make_model_factorized 24529 non-null int64 10 body_type__Convertible 24529 non-null int64 11 body_type__Coupe 24529 non-null int64 12 body_type__Off-Road/Pick-up 24529 non-null int64 13 body_type__Sedan 24529 non-null int64 14 body_type__Station_wagon 24529 non-null int64 15 warranty_Yes 24529 non-null int64 16 gearbox_Manual 24529 non-null int64 17 gearbox_Semi-automatic 24529 non-null int64 18 fuel_type_Diesel 24529 non-null int64 19 fuel_type_Electric 24529 non-null int64 20 fuel_type_LPG 24529 non-null int64 21 colour_Black 24529 non-null int64 22 colour_Blue 24529 non-null int64 23 colour_Bronze 24529 non-null int64 24 colour_Brown 24529 non-null int64 25 colour_Gold 24529 non-null int64 26 colour_Green 24529 non-null int64 27 colour_Grey 24529 non-null int64 28 colour_Orange 24529 non-null int64 29 colour_Red 24529 non-null int64 30 colour_Silver 24529 non-null int64 31 colour_Violet 24529 non-null int64 32 colour_White 24529 non-null int64 33 colour_Yellow 24529 non-null int64 34 drivetrain_Front 24529 non-null int64 35 drivetrain_Rear 24529 non-null int64 36 non_smoker_Yes 24529 non-null int64 37 emission_sticker_No_sticker 24529 non-null int64 38 emission_sticker_Red 24529 non-null int64 39 emission_sticker_Yellow 24529 non-null int64 40 upholstery_Full_leather 24529 non-null int64 41 upholstery_Other 24529 non-null int64 42 upholstery_Part_leather 24529 non-null int64 43 upholstery_Velour 24529 non-null int64 44 upholstery_alcantara 24529 non-null int64 45 safety_security_package_Basic 24529 non-null int64 46 safety_security_package_Enhanced 24529 non-null int64 47 comfort_convenience_package_Basic 24529 non-null int64 48 comfort_convenience_package_Enhanced 24529 non-null int64 49 ent_media_package_Basic 24529 non-null int64 50 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(51) memory usage: 9.5 MB
None
(24529, 51)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 HuberRegression Model Metrics : train_set test_set R2 0.74842 0.75294 mae 4644.22247 4622.21707 mse 56492043.07455 55071902.37974 rmse 7516.11888 7421.04456 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.84257 mae 1.14543 3307.79947 mse 1328.78718 35092847.19058 rmse 36.45253 5923.92161 Random Forest Regressor Model Metrics : train_set test_set R2 0.92145 0.88511 mae 2565.42971 2993.96732 mse 17639509.41618 25610097.71287 rmse 4199.94160 5060.64203 Ada Boost Regressor Model Metrics : train_set test_set R2 0.55878 0.53899 mae 8411.11711 8462.59512 mse 99077157.86718 102761542.31176 rmse 9953.75094 10137.13679 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.89748 0.87252 mae 3061.44094 3244.39116 mse 23020963.52384 28416434.69317 rmse 4798.01662 5330.70677 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97580 0.91265 mae 1657.36035 2539.00851 mse 5433075.53408 19469922.88249 rmse 2330.89587 4412.47356 [LightGBM] [Info] Total Bins 1322 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.94720 0.90983 mae 2283.47161 2674.31067 mse 11855808.80354 20100010.33039 rmse 3443.22651 4483.30351 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.76655 0.76659 mae 4839.14450 4850.98532 mse 52421491.42523 52028553.03376 rmse 7240.26874 7213.08208 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96380 0.92139 mae 1976.12172 2477.60347 mse 8127895.32365 17521714.02079 rmse 2850.94639 4185.89465 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8411.11711 | 4839.14450 | 1976.12172 | 1.14543 | 3061.44094 | 4644.22247 | 2283.47161 | 4841.22970 | 2565.42971 | 4841.22970 | 1657.36035 |
| MSE Score | 99077157.86718 | 52421491.42523 | 8127895.32365 | 1328.78718 | 23020963.52384 | 56492043.07455 | 11855808.80354 | 52412344.82297 | 17639509.41618 | 52412344.82297 | 5433075.53408 |
| R2 Score | 0.53899 | 0.76659 | 0.92139 | 0.84257 | 0.87252 | 0.75294 | 0.90983 | 0.76651 | 0.88511 | 0.76651 | 0.91265 |
| RMSE Score | 10137.13679 | 7213.08208 | 4185.89465 | 5923.92161 | 5330.70677 | 7421.04456 | 4483.30351 | 7214.29080 | 5060.64203 | 7214.29080 | 4412.47356 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< NurdanDA8123's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | seats | engine_size | gears | co2_emissions | power_kW | fuel_consumption_comb | age | make_model_factorized | body_type__Convertible | body_type__Coupe | body_type__Off-Road/Pick-up | body_type__Sedan | body_type__Station_wagon | warranty_Yes | gearbox_Manual | gearbox_Semi-automatic | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG | colour_Black | colour_Blue | colour_Bronze | colour_Brown | colour_Gold | colour_Green | colour_Grey | colour_Orange | colour_Red | colour_Silver | colour_Violet | colour_White | colour_Yellow | drivetrain_Front | drivetrain_Rear | non_smoker_Yes | emission_sticker_No_sticker | emission_sticker_Red | emission_sticker_Yellow | upholstery_Full_leather | upholstery_Other | upholstery_Part_leather | upholstery_Velour | upholstery_alcantara | safety_security_package_Basic | safety_security_package_Enhanced | comfort_convenience_package_Basic | comfort_convenience_package_Enhanced | ent_media_package_Basic | ent_media_package_Enhanced | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 21000 | 1000 | 5 | 1461 | 6 | 106 | 85 | 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 21150 | 4500 | 5 | 1332 | 6 | 131 | 96 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 25190 | 4018 | 5 | 1332 | 6 | 153 | 110 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 24222 | 8000 | 5 | 1332 | 6 | 121 | 110 | 5 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 24529 entries, 0 to 24528 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 24529 non-null int64 1 mileage 24529 non-null int64 2 seats 24529 non-null int64 3 engine_size 24529 non-null int64 4 gears 24529 non-null int64 5 co2_emissions 24529 non-null int64 6 power_kW 24529 non-null int64 7 fuel_consumption_comb 24529 non-null int64 8 age 24529 non-null int64 9 make_model_factorized 24529 non-null int64 10 body_type__Convertible 24529 non-null int64 11 body_type__Coupe 24529 non-null int64 12 body_type__Off-Road/Pick-up 24529 non-null int64 13 body_type__Sedan 24529 non-null int64 14 body_type__Station_wagon 24529 non-null int64 15 warranty_Yes 24529 non-null int64 16 gearbox_Manual 24529 non-null int64 17 gearbox_Semi-automatic 24529 non-null int64 18 fuel_type_Diesel 24529 non-null int64 19 fuel_type_Electric 24529 non-null int64 20 fuel_type_LPG 24529 non-null int64 21 colour_Black 24529 non-null int64 22 colour_Blue 24529 non-null int64 23 colour_Bronze 24529 non-null int64 24 colour_Brown 24529 non-null int64 25 colour_Gold 24529 non-null int64 26 colour_Green 24529 non-null int64 27 colour_Grey 24529 non-null int64 28 colour_Orange 24529 non-null int64 29 colour_Red 24529 non-null int64 30 colour_Silver 24529 non-null int64 31 colour_Violet 24529 non-null int64 32 colour_White 24529 non-null int64 33 colour_Yellow 24529 non-null int64 34 drivetrain_Front 24529 non-null int64 35 drivetrain_Rear 24529 non-null int64 36 non_smoker_Yes 24529 non-null int64 37 emission_sticker_No_sticker 24529 non-null int64 38 emission_sticker_Red 24529 non-null int64 39 emission_sticker_Yellow 24529 non-null int64 40 upholstery_Full_leather 24529 non-null int64 41 upholstery_Other 24529 non-null int64 42 upholstery_Part_leather 24529 non-null int64 43 upholstery_Velour 24529 non-null int64 44 upholstery_alcantara 24529 non-null int64 45 safety_security_package_Basic 24529 non-null int64 46 safety_security_package_Enhanced 24529 non-null int64 47 comfort_convenience_package_Basic 24529 non-null int64 48 comfort_convenience_package_Enhanced 24529 non-null int64 49 ent_media_package_Basic 24529 non-null int64 50 ent_media_package_Enhanced 24529 non-null int64 dtypes: int64(51) memory usage: 9.5 MB
None
(24529, 51)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 HuberRegression Model Metrics : train_set test_set R2 0.74842 0.75294 mae 4644.22247 4622.21707 mse 56492043.07455 55071902.37974 rmse 7516.11888 7421.04456 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.76659 0.76651 mae 4841.22970 4854.47431 mse 52412344.82297 52045991.71109 rmse 7239.63706 7214.29080 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99999 0.84257 mae 1.14543 3307.79947 mse 1328.78718 35092847.19058 rmse 36.45253 5923.92161 Random Forest Regressor Model Metrics : train_set test_set R2 0.92145 0.88511 mae 2565.42971 2993.96732 mse 17639509.41618 25610097.71287 rmse 4199.94160 5060.64203 Ada Boost Regressor Model Metrics : train_set test_set R2 0.55878 0.53899 mae 8411.11711 8462.59512 mse 99077157.86718 102761542.31176 rmse 9953.75094 10137.13679 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.89748 0.87252 mae 3061.44094 3244.39116 mse 23020963.52384 28416434.69317 rmse 4798.01662 5330.70677 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97580 0.91265 mae 1657.36035 2539.00851 mse 5433075.53408 19469922.88249 rmse 2330.89587 4412.47356 [LightGBM] [Info] Total Bins 1322 [LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50 [LightGBM] [Info] Start training from score 21429.045836 Light GBM Regressor Model Metrics : train_set test_set R2 0.94720 0.90983 mae 2283.47161 2674.31067 mse 11855808.80354 20100010.33039 rmse 3443.22651 4483.30351 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.76655 0.76659 mae 4839.14450 4850.98532 mse 52421491.42523 52028553.03376 rmse 7240.26874 7213.08208 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96380 0.92139 mae 1976.12172 2477.60347 mse 8127895.32365 17521714.02079 rmse 2850.94639 4185.89465 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8411.11711 | 4839.14450 | 1976.12172 | 1.14543 | 3061.44094 | 4644.22247 | 2283.47161 | 4841.22970 | 2565.42971 | 4841.22970 | 1657.36035 |
| MSE Score | 99077157.86718 | 52421491.42523 | 8127895.32365 | 1328.78718 | 23020963.52384 | 56492043.07455 | 11855808.80354 | 52412344.82297 | 17639509.41618 | 52412344.82297 | 5433075.53408 |
| R2 Score | 0.53899 | 0.76659 | 0.92139 | 0.84257 | 0.87252 | 0.75294 | 0.90983 | 0.76651 | 0.88511 | 0.76651 | 0.91265 |
| RMSE Score | 10137.13679 | 7213.08208 | 4185.89465 | 5923.92161 | 5330.70677 | 7421.04456 | 4483.30351 | 7214.29080 | 5060.64203 | 7214.29080 | 4412.47356 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< SerahsiDA8135's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | power | gears | age | make_model_encoded | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | seller_Private_seller | drivetrain_Front | drivetrain_Rear | emission_class_Other | previous_owner_Second_Hand | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 120200.00000 | 75.00000 | 6.00000 | 6.00000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 |
| 1 | 46990 | 18995.00000 | 225.00000 | 7.00000 | 2.00000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 16800 | 197000.00000 | 100.00000 | 7.00000 | 7.00000 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 |
| 3 | 4690 | 165000.00000 | 90.00000 | 6.00000 | 17.00000 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 |
| 4 | 22550 | 83339.00000 | 90.00000 | 7.00000 | 4.00000 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21595 entries, 0 to 21594 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 21595 non-null int64 1 mileage 21595 non-null float64 2 power 21595 non-null float64 3 gears 21595 non-null float64 4 age 21595 non-null float64 5 make_model_encoded 21595 non-null int64 6 make_Fiat 21595 non-null int64 7 make_Ford 21595 non-null int64 8 make_Hyundai 21595 non-null int64 9 make_Mercedes_Benz 21595 non-null int64 10 make_Nissan 21595 non-null int64 11 make_Opel 21595 non-null int64 12 make_Peugeot 21595 non-null int64 13 make_Renault 21595 non-null int64 14 make_Seat 21595 non-null int64 15 make_Skoda 21595 non-null int64 16 make_Toyota 21595 non-null int64 17 make_Volvo 21595 non-null int64 18 body_type_Convertible 21595 non-null int64 19 body_type_Coupe 21595 non-null int64 20 body_type_Off-Road/Pick-up 21595 non-null int64 21 body_type_Sedan 21595 non-null int64 22 body_type_Station_wagon 21595 non-null int64 23 gearbox_Manual 21595 non-null int64 24 fuel_type_Diesel 21595 non-null int64 25 fuel_type_Electric 21595 non-null int64 26 fuel_type_LPG/CNG 21595 non-null int64 27 fuel_type_Other 21595 non-null int64 28 seller_Private_seller 21595 non-null int64 29 drivetrain_Front 21595 non-null int64 30 drivetrain_Rear 21595 non-null int64 31 emission_class_Other 21595 non-null int64 32 previous_owner_Second_Hand 21595 non-null int64 33 engine_size_cat_High 21595 non-null int64 34 engine_size_cat_Low 21595 non-null int64 35 engine_size_cat_Medium 21595 non-null int64 36 comfort_convenience_cat_standard 21595 non-null int64 dtypes: float64(4), int64(33) memory usage: 6.1 MB
None
(21595, 37)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.85925 0.85440 mae 3402.34857 3417.53166 mse 22872699.57840 23820881.22452 rmse 4782.54112 4880.66401 HuberRegression Model Metrics : train_set test_set R2 0.85398 0.84773 mae 3317.66856 3349.87957 mse 23729535.09775 24912326.56239 rmse 4871.29707 4991.22496 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.85925 0.85440 mae 3402.34857 3417.53166 mse 22872699.57840 23820881.22452 rmse 4782.54112 4880.66401 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99981 0.87368 mae 18.10270 2911.60305 mse 31387.70910 20667313.64821 rmse 177.16577 4546.13172 Random Forest Regressor Model Metrics : train_set test_set R2 0.93185 0.90941 mae 2263.07513 2554.28258 mse 11075821.10222 14820676.25809 rmse 3328.03562 3849.76314 Ada Boost Regressor Model Metrics : train_set test_set R2 0.68922 0.68685 mae 6211.44093 6220.87469 mse 50505573.19470 51234335.79702 rmse 7106.72732 7157.81641 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.91853 0.90693 mae 2521.91759 2644.81616 mse 13239480.89893 15226621.74067 rmse 3638.60975 3902.13041 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97450 0.94083 mae 1494.12929 2086.18831 mse 4144312.27589 9680973.95705 rmse 2035.75840 3111.42635 [LightGBM] [Info] Total Bins 768 [LightGBM] [Info] Number of data points in the train set: 15116, number of used features: 36 [LightGBM] [Info] Start training from score 20967.344205 Light GBM Regressor Model Metrics : train_set test_set R2 0.95113 0.93218 mae 1962.72444 2210.20362 mse 7941155.28711 11096360.37160 rmse 2818.00555 3331.11999 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.85922 0.85421 mae 3402.85400 3419.05094 mse 22878997.86064 23853156.81437 rmse 4783.19954 4883.96937 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96417 0.94335 mae 1738.53207 2039.59588 mse 5821979.87276 9268519.37805 rmse 2412.87792 3044.42431 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6211.44093 | 3402.85400 | 1738.53207 | 18.10270 | 2521.91759 | 3317.66856 | 1962.72444 | 3402.34857 | 2263.07513 | 3402.34857 | 1494.12929 |
| MSE Score | 50505573.19470 | 22878997.86064 | 5821979.87276 | 31387.70910 | 13239480.89893 | 23729535.09775 | 7941155.28711 | 22872699.57840 | 11075821.10222 | 22872699.57840 | 4144312.27589 |
| R2 Score | 0.68685 | 0.85421 | 0.94335 | 0.87368 | 0.90693 | 0.84773 | 0.93218 | 0.85440 | 0.90941 | 0.85440 | 0.94083 |
| RMSE Score | 7157.81641 | 4883.96937 | 3044.42431 | 4546.13172 | 3902.13041 | 4991.22496 | 3331.11999 | 4880.66401 | 3849.76314 | 4880.66401 | 3111.42635 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< SezerDA8134's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
| price | mileage | power | gears | age | make_model_encoded | make_Fiat | make_Ford | make_Hyundai | make_Mercedes_Benz | make_Nissan | make_Opel | make_Peugeot | make_Renault | make_Seat | make_Skoda | make_Toyota | make_Volvo | location_BE | location_BG | location_DE | location_ES | location_FR | location_IT | location_LU | location_NL | body_type_Convertible | body_type_Coupe | body_type_Off-Road/Pick-up | body_type_Sedan | body_type_Station_wagon | gearbox_Manual | fuel_type_Diesel | fuel_type_Electric | fuel_type_LPG/CNG | fuel_type_Other | seller_Private_seller | drivetrain_Front | drivetrain_Rear | emission_class_Euro_2 | emission_class_Euro_3 | emission_class_Euro_4 | emission_class_Euro_5 | emission_class_Euro_6 | upholstery_Leather | upholstery_Other | previous_owner_Second_Hand | entertainment_media_count_Upgrated | engine_size_cat_High | engine_size_cat_Low | engine_size_cat_Medium | safety_security_category_Middle | safety_security_category_Premium | comfort_convenience_cat_standard | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16950 | 120200.00000 | 75.00000 | 6.00000 | 6.00000 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 1 | 46990 | 18995.00000 | 225.00000 | 7.00000 | 2.00000 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 2 | 16800 | 197000.00000 | 100.00000 | 7.00000 | 7.00000 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 3 | 4690 | 165000.00000 | 90.00000 | 6.00000 | 17.00000 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| 4 | 22550 | 83339.00000 | 90.00000 | 7.00000 | 4.00000 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 20194 entries, 0 to 20193 Data columns (total 54 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 price 20194 non-null int64 1 mileage 20194 non-null float64 2 power 20194 non-null float64 3 gears 20194 non-null float64 4 age 20194 non-null float64 5 make_model_encoded 20194 non-null int64 6 make_Fiat 20194 non-null int64 7 make_Ford 20194 non-null int64 8 make_Hyundai 20194 non-null int64 9 make_Mercedes_Benz 20194 non-null int64 10 make_Nissan 20194 non-null int64 11 make_Opel 20194 non-null int64 12 make_Peugeot 20194 non-null int64 13 make_Renault 20194 non-null int64 14 make_Seat 20194 non-null int64 15 make_Skoda 20194 non-null int64 16 make_Toyota 20194 non-null int64 17 make_Volvo 20194 non-null int64 18 location_BE 20194 non-null int64 19 location_BG 20194 non-null int64 20 location_DE 20194 non-null int64 21 location_ES 20194 non-null int64 22 location_FR 20194 non-null int64 23 location_IT 20194 non-null int64 24 location_LU 20194 non-null int64 25 location_NL 20194 non-null int64 26 body_type_Convertible 20194 non-null int64 27 body_type_Coupe 20194 non-null int64 28 body_type_Off-Road/Pick-up 20194 non-null int64 29 body_type_Sedan 20194 non-null int64 30 body_type_Station_wagon 20194 non-null int64 31 gearbox_Manual 20194 non-null int64 32 fuel_type_Diesel 20194 non-null int64 33 fuel_type_Electric 20194 non-null int64 34 fuel_type_LPG/CNG 20194 non-null int64 35 fuel_type_Other 20194 non-null int64 36 seller_Private_seller 20194 non-null int64 37 drivetrain_Front 20194 non-null int64 38 drivetrain_Rear 20194 non-null int64 39 emission_class_Euro_2 20194 non-null int64 40 emission_class_Euro_3 20194 non-null int64 41 emission_class_Euro_4 20194 non-null int64 42 emission_class_Euro_5 20194 non-null int64 43 emission_class_Euro_6 20194 non-null int64 44 upholstery_Leather 20194 non-null int64 45 upholstery_Other 20194 non-null int64 46 previous_owner_Second_Hand 20194 non-null int64 47 entertainment_media_count_Upgrated 20194 non-null int64 48 engine_size_cat_High 20194 non-null int64 49 engine_size_cat_Low 20194 non-null int64 50 engine_size_cat_Medium 20194 non-null int64 51 safety_security_category_Middle 20194 non-null int64 52 safety_security_category_Premium 20194 non-null int64 53 comfort_convenience_cat_standard 20194 non-null int64 dtypes: float64(4), int64(50) memory usage: 8.3 MB
None
(20194, 54)
There is no null value <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL SCORES >>>>>>>>>>>>>>>>>>>>>>>>>>>>> Linear Model Metrics : train_set test_set R2 0.86627 0.86246 mae 3081.78410 3143.04206 mse 19201743.31654 19906149.33951 rmse 4381.97938 4461.63079 HuberRegression Model Metrics : train_set test_set R2 0.86050 0.85630 mae 2996.86822 3066.55568 mse 20030121.31785 20797124.18570 rmse 4475.50235 4560.38641 WLS (Weighted Least Squares) Model Metrics : train_set test_set R2 0.86627 0.86246 mae 3081.78410 3143.04206 mse 19201743.31654 19906149.33951 rmse 4381.97938 4461.63079 Decision Tree Regressor Model Metrics : train_set test_set R2 0.99990 0.86469 mae 10.95616 2818.92312 mse 14508.61453 19582166.26063 rmse 120.45171 4425.17415 Random Forest Regressor Model Metrics : train_set test_set R2 0.92951 0.90706 mae 2164.77986 2467.61313 mse 10121751.41782 13451121.12256 rmse 3181.47001 3667.57701 Ada Boost Regressor Model Metrics : train_set test_set R2 0.68048 0.67711 mae 5874.90564 5863.01689 mse 45877247.74444 46729745.70760 rmse 6773.27452 6835.91586 Gradient Boost Regressor Model Metrics : train_set test_set R2 0.91858 0.90900 mae 2369.32179 2476.80098 mse 11690941.55805 13170387.55993 rmse 3419.20189 3629.10286 XG Boosting Regressor Model Metrics : train_set test_set R2 0.97727 0.94133 mae 1324.08738 1949.70763 mse 3262950.45071 8491681.87739 rmse 1806.36388 2914.04905 [LightGBM] [Info] Total Bins 769 [LightGBM] [Info] Number of data points in the train set: 14135, number of used features: 50 [LightGBM] [Info] Start training from score 20091.370994 Light GBM Regressor Model Metrics : train_set test_set R2 0.95433 0.93540 mae 1795.69834 2071.96371 mse 6557499.77470 9348762.89768 rmse 2560.76156 3057.57468 Bayesian Linear Ridge Model Metrics : train_set test_set R2 0.86617 0.86240 mae 3082.96580 3144.31155 mse 19215087.50191 19913925.70481 rmse 4383.50174 4462.50218 Cat Boost Linear Ridge Model Metrics : train_set test_set R2 0.96820 0.94654 mae 1543.28089 1878.72040 mse 4565293.85719 7737690.92461 rmse 2136.65483 2781.67053 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MODEL PERFORMANCE / COMPARISION >>>>>>>>>>>>>>>>>>>>>>>>>>>
| Supervised Models | ADABoost | Bayesian Ridge | CatBoost | Decision Tree | Gradient Boosting | Huber Regression | LightGBM | Linear Regression | Random Forest | Weighted Least Squares | XGBoost |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5874.90564 | 3082.96580 | 1543.28089 | 10.95616 | 2369.32179 | 2996.86822 | 1795.69834 | 3081.78410 | 2164.77986 | 3081.78410 | 1324.08738 |
| MSE Score | 45877247.74444 | 19215087.50191 | 4565293.85719 | 14508.61453 | 11690941.55805 | 20030121.31785 | 6557499.77470 | 19201743.31654 | 10121751.41782 | 19201743.31654 | 3262950.45071 |
| R2 Score | 0.67711 | 0.86240 | 0.94654 | 0.86469 | 0.90900 | 0.85630 | 0.93540 | 0.86246 | 0.90706 | 0.86246 | 0.94133 |
| RMSE Score | 6835.91586 | 4462.50218 | 2781.67053 | 4425.17415 | 3629.10286 | 4560.38641 | 3057.57468 | 4461.63079 | 3667.57701 | 4461.63079 | 2914.04905 |
Model Training by Pipelines:
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< TugceDA8122's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 808
[LightGBM] [Info] Number of data points in the train set: 17281, number of used features: 35
[LightGBM] [Info] Start training from score 20969.221168
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.85460 | 3430.44768 | 23025314.77964 | 4798.47005 | 0.85572 | 0.85460 |
| 1 | Decision Tree Regressor | 0.87928 | 2862.54220 | 19117210.83807 | 4372.32328 | 0.99981 | 0.87928 |
| 2 | Random Forest Regressor | 0.93257 | 2135.39459 | 10678858.12727 | 3267.85222 | 0.99094 | 0.93257 |
| 3 | AdaBoost Regressor | 0.64743 | 6513.42438 | 55833400.06720 | 7472.17506 | 0.66652 | 0.64743 |
| 4 | Gradient Boosting Regressor | 0.90798 | 2607.45976 | 14572142.13428 | 3817.34753 | 0.91474 | 0.90798 |
| 5 | XGB Regressor | 0.94044 | 2063.19448 | 9432105.17867 | 3071.17326 | 0.97344 | 0.94044 |
| 6 | LGBM Regressor | 0.93472 | 2183.33711 | 10338079.24885 | 3215.28836 | 0.94938 | 0.93472 |
| 7 | Bayesian Ridge | 0.85455 | 3431.01707 | 23033629.26358 | 4799.33634 | 0.85571 | 0.85455 |
| 8 | CatBoost Regressor | 0.94290 | 2028.32026 | 9041531.17330 | 3006.91390 | 0.96259 | 0.94290 |
| 9 | SVM Regressor | 0.01729 | 9062.79313 | 155620640.85701 | 12474.80023 | 0.01429 | 0.01729 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6513.42438 | 3431.01707 | 2028.32026 | 2862.54220 | 2607.45976 | 2183.33711 | 3430.44768 | 2135.39459 | 9062.79313 | 2063.19448 |
| MSE Score | 55833400.06720 | 23033629.26358 | 9041531.17330 | 19117210.83807 | 14572142.13428 | 10338079.24885 | 23025314.77964 | 10678858.12727 | 155620640.85701 | 9432105.17867 |
| R2 Score | 0.64743 | 0.85455 | 0.94290 | 0.87928 | 0.90798 | 0.93472 | 0.85460 | 0.93257 | 0.01729 | 0.94044 |
| RMSE Score | 7472.17506 | 4799.33634 | 3006.91390 | 4372.32328 | 3817.34753 | 3215.28836 | 4798.47005 | 3267.85222 | 12474.80023 | 3071.17326 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< TugceDA8122's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1137
[LightGBM] [Info] Number of data points in the train set: 16640, number of used features: 61
[LightGBM] [Info] Start training from score 20502.329087
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.87218 | 3045.40187 | 19043295.54805 | 4363.86246 | 0.87682 | 0.87218 |
| 1 | Decision Tree Regressor | 0.86923 | 2798.95658 | 19482718.60394 | 4413.92327 | 0.99995 | 0.86923 |
| 2 | Random Forest Regressor | 0.93633 | 1972.68535 | 9486099.04041 | 3079.95114 | 0.99142 | 0.93633 |
| 3 | AdaBoost Regressor | 0.72096 | 5597.94787 | 41571284.24741 | 6447.57972 | 0.72739 | 0.72096 |
| 4 | Gradient Boosting Regressor | 0.91382 | 2422.85674 | 12839518.55046 | 3583.22739 | 0.92530 | 0.91382 |
| 5 | XGB Regressor | 0.94150 | 1918.17226 | 8715621.42349 | 2952.22313 | 0.97911 | 0.94150 |
| 6 | LGBM Regressor | 0.93890 | 1986.63867 | 9103296.99098 | 3017.16705 | 0.95869 | 0.93890 |
| 7 | Bayesian Ridge | 0.87210 | 3046.42342 | 19054650.70839 | 4365.16331 | 0.87679 | 0.87210 |
| 8 | CatBoost Regressor | 0.94934 | 1805.61014 | 7547825.27947 | 2747.33057 | 0.97147 | 0.94934 |
| 9 | SVM Regressor | 0.00359 | 8916.61725 | 148444628.79986 | 12183.78549 | 0.00434 | 0.00359 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5597.94787 | 3046.42342 | 1805.61014 | 2798.95658 | 2422.85674 | 1986.63867 | 3045.40187 | 1972.68535 | 8916.61725 | 1918.17226 |
| MSE Score | 41571284.24741 | 19054650.70839 | 7547825.27947 | 19482718.60394 | 12839518.55046 | 9103296.99098 | 19043295.54805 | 9486099.04041 | 148444628.79986 | 8715621.42349 |
| R2 Score | 0.72096 | 0.87210 | 0.94934 | 0.86923 | 0.91382 | 0.93890 | 0.87218 | 0.93633 | 0.00359 | 0.94150 |
| RMSE Score | 6447.57972 | 4365.16331 | 2747.33057 | 4413.92327 | 3583.22739 | 3017.16705 | 4363.86246 | 3079.95114 | 12183.78549 | 2952.22313 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< AsliDA8115's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.76535 | 4847.86414 | 52491299.42002 | 7245.08795 | 0.76709 | 0.76535 |
| 1 | Decision Tree Regressor | 0.83591 | 3343.81618 | 36705363.21993 | 6058.49513 | 0.99999 | 0.83591 |
| 2 | Random Forest Regressor | 0.92269 | 2386.58850 | 17293597.15240 | 4158.55710 | 0.98841 | 0.92269 |
| 3 | AdaBoost Regressor | 0.48863 | 9345.22956 | 114391814.98679 | 10695.41093 | 0.48707 | 0.48863 |
| 4 | Gradient Boosting Regressor | 0.88034 | 3191.59254 | 26767061.30581 | 5173.68933 | 0.89266 | 0.88034 |
| 5 | XGB Regressor | 0.91894 | 2516.52315 | 18132091.42636 | 4258.17936 | 0.97550 | 0.91894 |
| 6 | LGBM Regressor | 0.91622 | 2650.53939 | 18741923.55600 | 4329.19433 | 0.94453 | 0.91622 |
| 7 | Bayesian Ridge | 0.76532 | 4846.54654 | 52498176.46425 | 7245.56254 | 0.76708 | 0.76532 |
| 8 | CatBoost Regressor | 0.92820 | 2422.45516 | 16060754.07656 | 4007.58706 | 0.96302 | 0.92820 |
| 9 | SVM Regressor | -0.02998 | 10299.77303 | 230403540.97345 | 15179.04941 | -0.02267 | -0.02998 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 9345.22956 | 4846.54654 | 2422.45516 | 3343.81618 | 3191.59254 | 2650.53939 | 4847.86414 | 2386.58850 | 10299.77303 | 2516.52315 |
| MSE Score | 114391814.98679 | 52498176.46425 | 16060754.07656 | 36705363.21993 | 26767061.30581 | 18741923.55600 | 52491299.42002 | 17293597.15240 | 230403540.97345 | 18132091.42636 |
| R2 Score | 0.48863 | 0.76532 | 0.92820 | 0.83591 | 0.88034 | 0.91622 | 0.76535 | 0.92269 | -0.02998 | 0.91894 |
| RMSE Score | 10695.41093 | 7245.56254 | 4007.58706 | 6058.49513 | 5173.68933 | 4329.19433 | 7245.08795 | 4158.55710 | 15179.04941 | 4258.17936 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< AysegulDA8116's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> !!!!!!!!AysegulDA8116's DataFrame has non-numeric value !!!!!!!!!! <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< DamlaDA8120's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> [LightGBM] [Info] Total Bins 1503 [LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 63 [LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.87947 | 3842.81604 | 26962198.98562 | 5192.51374 | 0.87789 | 0.87947 |
| 1 | Decision Tree Regressor | 0.90444 | 2862.71722 | 21376495.90289 | 4623.47228 | 0.99999 | 0.90444 |
| 2 | Random Forest Regressor | 0.95703 | 2019.01618 | 9612429.43105 | 3100.39182 | 0.99343 | 0.95703 |
| 3 | AdaBoost Regressor | 0.82715 | 5210.22303 | 38665305.06620 | 6218.14322 | 0.82302 | 0.82715 |
| 4 | Gradient Boosting Regressor | 0.93477 | 2683.24893 | 14590916.16058 | 3819.80578 | 0.93713 | 0.93477 |
| 5 | XGB Regressor | 0.95583 | 2113.45031 | 9881606.73771 | 3143.50230 | 0.98095 | 0.95583 |
| 6 | LGBM Regressor | 0.95344 | 2234.08197 | 10415015.87701 | 3227.23037 | 0.96279 | 0.95344 |
| 7 | Bayesian Ridge | 0.87943 | 3842.85125 | 26971506.82387 | 5193.40994 | 0.87789 | 0.87943 |
| 8 | CatBoost Regressor | 0.95770 | 2091.43829 | 9461908.46833 | 3076.02153 | 0.97327 | 0.95770 |
| 9 | SVM Regressor | -0.03420 | 10316.35794 | 231347282.93949 | 15210.10463 | -0.02653 | -0.03420 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5210.22303 | 3842.85125 | 2091.43829 | 2862.71722 | 2683.24893 | 2234.08197 | 3842.81604 | 2019.01618 | 10316.35794 | 2113.45031 |
| MSE Score | 38665305.06620 | 26971506.82387 | 9461908.46833 | 21376495.90289 | 14590916.16058 | 10415015.87701 | 26962198.98562 | 9612429.43105 | 231347282.93949 | 9881606.73771 |
| R2 Score | 0.82715 | 0.87943 | 0.95770 | 0.90444 | 0.93477 | 0.95344 | 0.87947 | 0.95703 | -0.03420 | 0.95583 |
| RMSE Score | 6218.14322 | 5193.40994 | 3076.02153 | 4623.47228 | 3819.80578 | 3227.23037 | 5192.51374 | 3100.39182 | 15210.10463 | 3143.50230 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EmreDA8119's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 835
[LightGBM] [Info] Number of data points in the train set: 17293, number of used features: 45
[LightGBM] [Info] Start training from score 20940.150061
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.86116 | 3368.80822 | 22120132.79164 | 4703.20452 | 0.86497 | 0.86116 |
| 1 | Decision Tree Regressor | 0.88210 | 2812.94500 | 18783045.11448 | 4333.94106 | 0.99985 | 0.88210 |
| 2 | Random Forest Regressor | 0.93559 | 2102.70655 | 10261299.97685 | 3203.32639 | 0.99132 | 0.93559 |
| 3 | AdaBoost Regressor | 0.65439 | 6409.97950 | 55063371.01812 | 7420.46973 | 0.66772 | 0.65439 |
| 4 | Gradient Boosting Regressor | 0.90597 | 2650.65952 | 14980440.26891 | 3870.45737 | 0.91606 | 0.90597 |
| 5 | XGB Regressor | 0.94387 | 2028.04336 | 8943210.14555 | 2990.52005 | 0.97562 | 0.94387 |
| 6 | LGBM Regressor | 0.93519 | 2187.83743 | 10326205.98676 | 3213.44146 | 0.95194 | 0.93519 |
| 7 | Bayesian Ridge | 0.86113 | 3368.29641 | 22124596.02071 | 4703.67899 | 0.86495 | 0.86113 |
| 8 | CatBoost Regressor | 0.94717 | 1975.71096 | 8417152.36143 | 2901.23290 | 0.96574 | 0.94717 |
| 9 | SVM Regressor | 0.00609 | 9139.59998 | 158349556.07574 | 12583.70200 | 0.00693 | 0.00609 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6409.97950 | 3368.29641 | 1975.71096 | 2812.94500 | 2650.65952 | 2187.83743 | 3368.80822 | 2102.70655 | 9139.59998 | 2028.04336 |
| MSE Score | 55063371.01812 | 22124596.02071 | 8417152.36143 | 18783045.11448 | 14980440.26891 | 10326205.98676 | 22120132.79164 | 10261299.97685 | 158349556.07574 | 8943210.14555 |
| R2 Score | 0.65439 | 0.86113 | 0.94717 | 0.88210 | 0.90597 | 0.93519 | 0.86116 | 0.93559 | 0.00609 | 0.94387 |
| RMSE Score | 7420.46973 | 4703.67899 | 2901.23290 | 4333.94106 | 3870.45737 | 3213.44146 | 4703.20452 | 3203.32639 | 12583.70200 | 2990.52005 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EmreDA8127's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.76535 | 4847.86414 | 52491299.42002 | 7245.08795 | 0.76709 | 0.76535 |
| 1 | Decision Tree Regressor | 0.83256 | 3366.74759 | 37456168.81435 | 6120.14451 | 0.99999 | 0.83256 |
| 2 | Random Forest Regressor | 0.92246 | 2381.75686 | 17344517.50580 | 4164.67496 | 0.98845 | 0.92246 |
| 3 | AdaBoost Regressor | 0.51817 | 8934.71561 | 107784054.58741 | 10381.90997 | 0.51803 | 0.51817 |
| 4 | Gradient Boosting Regressor | 0.88066 | 3190.42327 | 26695048.58120 | 5166.72513 | 0.89266 | 0.88066 |
| 5 | XGB Regressor | 0.91894 | 2516.52315 | 18132091.42636 | 4258.17936 | 0.97550 | 0.91894 |
| 6 | LGBM Regressor | 0.91622 | 2650.53939 | 18741923.55600 | 4329.19433 | 0.94453 | 0.91622 |
| 7 | Bayesian Ridge | 0.76532 | 4846.54654 | 52498176.46425 | 7245.56254 | 0.76708 | 0.76532 |
| 8 | CatBoost Regressor | 0.92820 | 2422.45516 | 16060754.07656 | 4007.58706 | 0.96302 | 0.92820 |
| 9 | SVM Regressor | -0.02998 | 10299.77303 | 230403540.97345 | 15179.04941 | -0.02267 | -0.02998 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8934.71561 | 4846.54654 | 2422.45516 | 3366.74759 | 3190.42327 | 2650.53939 | 4847.86414 | 2381.75686 | 10299.77303 | 2516.52315 |
| MSE Score | 107784054.58741 | 52498176.46425 | 16060754.07656 | 37456168.81435 | 26695048.58120 | 18741923.55600 | 52491299.42002 | 17344517.50580 | 230403540.97345 | 18132091.42636 |
| R2 Score | 0.51817 | 0.76532 | 0.92820 | 0.83256 | 0.88066 | 0.91622 | 0.76535 | 0.92246 | -0.02998 | 0.91894 |
| RMSE Score | 10381.90997 | 7245.56254 | 4007.58706 | 6120.14451 | 5166.72513 | 4329.19433 | 7245.08795 | 4164.67496 | 15179.04941 | 4258.17936 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< EsraDA8133's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 599
[LightGBM] [Info] Number of data points in the train set: 17291, number of used features: 38
[LightGBM] [Info] Start training from score 20996.104505
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.85939 | 3383.26925 | 22270795.24241 | 4719.19434 | 0.85347 | 0.85939 |
| 1 | Decision Tree Regressor | 0.86805 | 2998.04781 | 20898706.10578 | 4571.51027 | 0.99979 | 0.86805 |
| 2 | Random Forest Regressor | 0.93022 | 2213.16567 | 11052513.76319 | 3324.53211 | 0.98984 | 0.93022 |
| 3 | AdaBoost Regressor | 0.63631 | 6662.76164 | 57605047.00101 | 7589.79888 | 0.65450 | 0.63631 |
| 4 | Gradient Boosting Regressor | 0.91145 | 2594.21352 | 14024854.84364 | 3744.97728 | 0.91121 | 0.91145 |
| 5 | XGB Regressor | 0.93139 | 2236.12026 | 10867216.01730 | 3296.54607 | 0.96761 | 0.93139 |
| 6 | LGBM Regressor | 0.93045 | 2282.51993 | 11016419.55072 | 3319.09921 | 0.94226 | 0.93045 |
| 7 | Bayesian Ridge | 0.85939 | 3382.46532 | 22270593.35424 | 4719.17295 | 0.85347 | 0.85939 |
| 8 | CatBoost Regressor | 0.93996 | 2119.18171 | 9509935.40723 | 3083.81832 | 0.95622 | 0.93996 |
| 9 | SVM Regressor | 0.02034 | 9144.93910 | 155167703.06966 | 12456.63289 | 0.01222 | 0.02034 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6662.76164 | 3382.46532 | 2119.18171 | 2998.04781 | 2594.21352 | 2282.51993 | 3383.26925 | 2213.16567 | 9144.93910 | 2236.12026 |
| MSE Score | 57605047.00101 | 22270593.35424 | 9509935.40723 | 20898706.10578 | 14024854.84364 | 11016419.55072 | 22270795.24241 | 11052513.76319 | 155167703.06966 | 10867216.01730 |
| R2 Score | 0.63631 | 0.85939 | 0.93996 | 0.86805 | 0.91145 | 0.93045 | 0.85939 | 0.93022 | 0.02034 | 0.93139 |
| RMSE Score | 7589.79888 | 4719.17295 | 3083.81832 | 4571.51027 | 3744.97728 | 3319.09921 | 4719.19434 | 3324.53211 | 12456.63289 | 3296.54607 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< GyulferaDA8131's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.76535 | 4847.86414 | 52491299.42002 | 7245.08795 | 0.76709 | 0.76535 |
| 1 | Decision Tree Regressor | 0.83980 | 3332.97255 | 35836587.50926 | 5986.36680 | 0.99999 | 0.83980 |
| 2 | Random Forest Regressor | 0.92144 | 2393.00096 | 17572866.16310 | 4192.00026 | 0.98859 | 0.92144 |
| 3 | AdaBoost Regressor | 0.49972 | 9137.91688 | 111910030.33520 | 10578.75372 | 0.50243 | 0.49972 |
| 4 | Gradient Boosting Regressor | 0.88050 | 3190.66494 | 26731434.60854 | 5170.24512 | 0.89266 | 0.88050 |
| 5 | XGB Regressor | 0.91894 | 2516.52315 | 18132091.42636 | 4258.17936 | 0.97550 | 0.91894 |
| 6 | LGBM Regressor | 0.91622 | 2650.53939 | 18741923.55600 | 4329.19433 | 0.94453 | 0.91622 |
| 7 | Bayesian Ridge | 0.76532 | 4846.54654 | 52498176.46425 | 7245.56254 | 0.76708 | 0.76532 |
| 8 | CatBoost Regressor | 0.92820 | 2422.45516 | 16060754.07656 | 4007.58706 | 0.96302 | 0.92820 |
| 9 | SVM Regressor | -0.02998 | 10299.77303 | 230403540.97345 | 15179.04941 | -0.02267 | -0.02998 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 9137.91688 | 4846.54654 | 2422.45516 | 3332.97255 | 3190.66494 | 2650.53939 | 4847.86414 | 2393.00096 | 10299.77303 | 2516.52315 |
| MSE Score | 111910030.33520 | 52498176.46425 | 16060754.07656 | 35836587.50926 | 26731434.60854 | 18741923.55600 | 52491299.42002 | 17572866.16310 | 230403540.97345 | 18132091.42636 |
| R2 Score | 0.49972 | 0.76532 | 0.92820 | 0.83980 | 0.88050 | 0.91622 | 0.76535 | 0.92144 | -0.02998 | 0.91894 |
| RMSE Score | 10578.75372 | 7245.56254 | 4007.58706 | 5986.36680 | 5170.24512 | 4329.19433 | 7245.08795 | 4192.00026 | 15179.04941 | 4258.17936 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HasanDA8121's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.76535 | 4847.86414 | 52491299.42002 | 7245.08795 | 0.76709 | 0.76535 |
| 1 | Decision Tree Regressor | 0.83681 | 3359.63793 | 36506034.45225 | 6042.02238 | 0.99999 | 0.83681 |
| 2 | Random Forest Regressor | 0.92295 | 2374.92717 | 17236512.51483 | 4151.68791 | 0.98883 | 0.92295 |
| 3 | AdaBoost Regressor | 0.54249 | 8666.37208 | 102344535.27988 | 10116.54760 | 0.54292 | 0.54249 |
| 4 | Gradient Boosting Regressor | 0.88075 | 3189.91670 | 26676135.36178 | 5164.89452 | 0.89266 | 0.88075 |
| 5 | XGB Regressor | 0.91894 | 2516.52315 | 18132091.42636 | 4258.17936 | 0.97550 | 0.91894 |
| 6 | LGBM Regressor | 0.91622 | 2650.53939 | 18741923.55600 | 4329.19433 | 0.94453 | 0.91622 |
| 7 | Bayesian Ridge | 0.76532 | 4846.54654 | 52498176.46425 | 7245.56254 | 0.76708 | 0.76532 |
| 8 | CatBoost Regressor | 0.92820 | 2422.45516 | 16060754.07656 | 4007.58706 | 0.96302 | 0.92820 |
| 9 | SVM Regressor | -0.02998 | 10299.77303 | 230403540.97345 | 15179.04941 | -0.02267 | -0.02998 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 8666.37208 | 4846.54654 | 2422.45516 | 3359.63793 | 3189.91670 | 2650.53939 | 4847.86414 | 2374.92717 | 10299.77303 | 2516.52315 |
| MSE Score | 102344535.27988 | 52498176.46425 | 16060754.07656 | 36506034.45225 | 26676135.36178 | 18741923.55600 | 52491299.42002 | 17236512.51483 | 230403540.97345 | 18132091.42636 |
| R2 Score | 0.54249 | 0.76532 | 0.92820 | 0.83681 | 0.88075 | 0.91622 | 0.76535 | 0.92295 | -0.02998 | 0.91894 |
| RMSE Score | 10116.54760 | 7245.56254 | 4007.58706 | 6042.02238 | 5164.89452 | 4329.19433 | 7245.08795 | 4151.68791 | 15179.04941 | 4258.17936 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< NurdanDA8123's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.76535 | 4847.86414 | 52491299.42002 | 7245.08795 | 0.76709 | 0.76535 |
| 1 | Decision Tree Regressor | 0.83390 | 3345.34261 | 37156805.94409 | 6095.63827 | 0.99999 | 0.83390 |
| 2 | Random Forest Regressor | 0.92284 | 2368.67530 | 17259349.84446 | 4154.43737 | 0.98859 | 0.92284 |
| 3 | AdaBoost Regressor | 0.49683 | 9269.38755 | 112558172.12103 | 10609.34362 | 0.49537 | 0.49683 |
| 4 | Gradient Boosting Regressor | 0.88051 | 3190.60209 | 26728942.82915 | 5170.00414 | 0.89266 | 0.88051 |
| 5 | XGB Regressor | 0.91894 | 2516.52315 | 18132091.42636 | 4258.17936 | 0.97550 | 0.91894 |
| 6 | LGBM Regressor | 0.91622 | 2650.53939 | 18741923.55600 | 4329.19433 | 0.94453 | 0.91622 |
| 7 | Bayesian Ridge | 0.76532 | 4846.54654 | 52498176.46425 | 7245.56254 | 0.76708 | 0.76532 |
| 8 | CatBoost Regressor | 0.92820 | 2422.45516 | 16060754.07656 | 4007.58706 | 0.96302 | 0.92820 |
| 9 | SVM Regressor | -0.02998 | 10299.77303 | 230403540.97345 | 15179.04941 | -0.02267 | -0.02998 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 9269.38755 | 4846.54654 | 2422.45516 | 3345.34261 | 3190.60209 | 2650.53939 | 4847.86414 | 2368.67530 | 10299.77303 | 2516.52315 |
| MSE Score | 112558172.12103 | 52498176.46425 | 16060754.07656 | 37156805.94409 | 26728942.82915 | 18741923.55600 | 52491299.42002 | 17259349.84446 | 230403540.97345 | 18132091.42636 |
| R2 Score | 0.49683 | 0.76532 | 0.92820 | 0.83390 | 0.88051 | 0.91622 | 0.76535 | 0.92284 | -0.02998 | 0.91894 |
| RMSE Score | 10609.34362 | 7245.56254 | 4007.58706 | 6095.63827 | 5170.00414 | 4329.19433 | 7245.08795 | 4154.43737 | 15179.04941 | 4258.17936 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< SerahsiDA8135's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 810
[LightGBM] [Info] Number of data points in the train set: 17276, number of used features: 36
[LightGBM] [Info] Start training from score 20980.283283
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.85711 | 3425.94182 | 22991544.44568 | 4794.94989 | 0.85803 | 0.85711 |
| 1 | Decision Tree Regressor | 0.87497 | 2916.00390 | 20117380.73230 | 4485.24032 | 0.99975 | 0.87497 |
| 2 | Random Forest Regressor | 0.93534 | 2160.72752 | 10403286.69695 | 3225.41264 | 0.99064 | 0.93534 |
| 3 | AdaBoost Regressor | 0.67962 | 6242.86881 | 51549810.98551 | 7179.81970 | 0.68950 | 0.67962 |
| 4 | Gradient Boosting Regressor | 0.91387 | 2573.10671 | 13857960.41360 | 3722.62816 | 0.91646 | 0.91387 |
| 5 | XGB Regressor | 0.94052 | 2095.74801 | 9571242.05515 | 3093.74240 | 0.97335 | 0.94052 |
| 6 | LGBM Regressor | 0.93528 | 2212.02271 | 10413402.70582 | 3226.98043 | 0.95006 | 0.93528 |
| 7 | Bayesian Ridge | 0.85704 | 3425.95602 | 23002084.60878 | 4796.04885 | 0.85802 | 0.85704 |
| 8 | CatBoost Regressor | 0.94395 | 2048.07543 | 9018285.09373 | 3003.04597 | 0.96324 | 0.94395 |
| 9 | SVM Regressor | 0.01884 | 9164.00477 | 157871298.70750 | 12564.68458 | 0.01264 | 0.01884 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 6242.86881 | 3425.95602 | 2048.07543 | 2916.00390 | 2573.10671 | 2212.02271 | 3425.94182 | 2160.72752 | 9164.00477 | 2095.74801 |
| MSE Score | 51549810.98551 | 23002084.60878 | 9018285.09373 | 20117380.73230 | 13857960.41360 | 10413402.70582 | 22991544.44568 | 10403286.69695 | 157871298.70750 | 9571242.05515 |
| R2 Score | 0.67962 | 0.85704 | 0.94395 | 0.87497 | 0.91387 | 0.93528 | 0.85711 | 0.93534 | 0.01884 | 0.94052 |
| RMSE Score | 7179.81970 | 4796.04885 | 3003.04597 | 4485.24032 | 3722.62816 | 3226.98043 | 4794.94989 | 3225.41264 | 12564.68458 | 3093.74240 |
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< SezerDA8134's WORK >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
[LightGBM] [Info] Total Bins 826
[LightGBM] [Info] Number of data points in the train set: 16155, number of used features: 50
[LightGBM] [Info] Start training from score 20101.304240
| Regression Models | R2 Score | MAE Score | MSE Score | RMSE Score | Train-Set R2 Score | Test-Set R2 Score | |
|---|---|---|---|---|---|---|---|
| 0 | Linear Regression | 0.85638 | 3149.71584 | 20165033.00258 | 4490.54930 | 0.86728 | 0.85638 |
| 1 | Decision Tree Regressor | 0.86296 | 2728.23744 | 19241907.85753 | 4386.55991 | 0.99984 | 0.86296 |
| 2 | Random Forest Regressor | 0.92932 | 2031.29425 | 9923362.92751 | 3150.13697 | 0.99073 | 0.92932 |
| 3 | AdaBoost Regressor | 0.67402 | 5848.55769 | 45770506.88789 | 6765.39037 | 0.69588 | 0.67402 |
| 4 | Gradient Boosting Regressor | 0.90560 | 2491.45746 | 13255066.94846 | 3640.75088 | 0.91922 | 0.90560 |
| 5 | XGB Regressor | 0.94187 | 1921.59651 | 8161485.78555 | 2856.83142 | 0.97664 | 0.94187 |
| 6 | LGBM Regressor | 0.93480 | 2067.50864 | 9155117.74373 | 3025.74251 | 0.95344 | 0.93480 |
| 7 | Bayesian Ridge | 0.85621 | 3150.30680 | 20188581.34207 | 4493.17052 | 0.86726 | 0.85621 |
| 8 | CatBoost Regressor | 0.94710 | 1855.11993 | 7427973.24255 | 2725.43084 | 0.96701 | 0.94710 |
| 9 | SVM Regressor | 0.01552 | 8558.74385 | 138228278.83645 | 11757.05230 | 0.01452 | 0.01552 |
| Regression Models | AdaBoost Regressor | Bayesian Ridge | CatBoost Regressor | Decision Tree Regressor | Gradient Boosting Regressor | LGBM Regressor | Linear Regression | Random Forest Regressor | SVM Regressor | XGB Regressor |
|---|---|---|---|---|---|---|---|---|---|---|
| MAE Score | 5848.55769 | 3150.30680 | 1855.11993 | 2728.23744 | 2491.45746 | 2067.50864 | 3149.71584 | 2031.29425 | 8558.74385 | 1921.59651 |
| MSE Score | 45770506.88789 | 20188581.34207 | 7427973.24255 | 19241907.85753 | 13255066.94846 | 9155117.74373 | 20165033.00258 | 9923362.92751 | 138228278.83645 | 8161485.78555 |
| R2 Score | 0.67402 | 0.85621 | 0.94710 | 0.86296 | 0.90560 | 0.93480 | 0.85638 | 0.92932 | 0.01552 | 0.94187 |
| RMSE Score | 6765.39037 | 4493.17052 | 2725.43084 | 4386.55991 | 3640.75088 | 3025.74251 | 4490.54930 | 3150.13697 | 11757.05230 | 2856.83142 |




